This dataset contains all data collected during the study, "How Are Different Asynchronous Programming Constructs in JavaScript Related to Software Quality? A Repository Mining Study on GitHub".

The Debt Collection Software Market has experienced robust growth driven by increasing demand for automated collection processes across various industries. Financial institutions, healthcare providers, and telecom companies are adopting advanced debt collection software to boost recovery rates and enhance efficiency. The global debt collection software market valuation reached USD 4.88 Billion in 2024 and is projected to expand to approximately USD 10.76 Billion by 2032.The integration of artificial intelligence and machine learning capabilities within debt collection platforms is revolutionizing the industry by enhancing debtor segmentation, payment prediction, and communication strategies. The increasing technological advancement and digital transformation across collection agencies and financial organizations is expected to propel the market to grow at a CAGR of 10.3% from 2026 to 2032.

A collection of 4 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

In this fMRI study, we examined whether the DMN’s response to task switches depends on the complexity of the active set of tasks, manipulated by the number of tasks in a run, or abstract task groupings based on instructional order. This collection contains wholebrain activation maps for the task switch conditions contrasted against task repeat conditions.

According to a questionnaire on the status of the data collection of the transport industry in the Arab region, Tunisia scored 14 points in the data collection of the quantity of air cargo and mail transported in the country between 2005 and 2018, which was the highest among the Middle East and North Africa (MENA) region. The road network length in Egypt was the highest in the MENA region at about 188 thousand kilometers in 2018.

According to a questionnaire on the status of the data collection of the transport industry in the Arab region, Tunisia scored ** points in the data collection of the number of passengers of air transport arrivals in the country between 2005 and 2018, which was the highest among the Middle East and North Africa (MENA) region. The road network length in Egypt was the highest in the MENA region at about *** thousand kilometers in 2018.

Burst mode low frequency, high amplitued calibrated electric field data from selected time periods

Online Financial Debt Collection Solutions Market size was valued at USD 5.74 Billion in 2024 and is projected to reach USD 12.52 Billion by 2032, growing at a CAGR of 10.4% during the forecast period 2026-2032.The acceleration of digital transformation across the financial services sector has been witnessed globally. Legacy debt collection systems are being replaced with cloud-based solutions that offer enhanced efficiency and reduced operational costs.An increase in consumer debt levels, particularly in developed economies, has been observed in recent years. Financial institutions and collection agencies are increasingly required to manage larger volumes of delinquent accounts, necessitating more sophisticated collection solutions.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling and person

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Every separate and independent structure that has been constructed or converted for use as temporary or permanent housing. This includes any class of fixed or mobile shelter used as a place of lodging at the time of enumeration. A dwelling can be a) a private house, apartment, floor in a house, room or group of rooms, ranch, etc. designed to give lodging to one person or a group of people or b) a boat, vehicle, railroad car, barn, shed, or any other type of shelter occupied as a place of lodging at the time of enumeration. - Households: All the occupying members of a family or private dwelling that live together as family. In most cases, a household is made up of a head of the family, relatives of this person (wife or partner, children, grand-children, nieces and nephews, etc.), close friends, guests, lodgers, domestic employees and all other occupants. Households with five or fewer lodgers are considered private,but households with six or more lodgers are considered a non-family group. - Group quarters: Accommodation for a group of people who are not usually connected by kinship ties who live together for reasons of discipline, healthcare, education, mlitary activity, religion, work or other dwellings such as reform schools, boarding schools, barracks, hopsitals, guest houses, nursing homes, workers camps, etc.

Universe

Population in private and communal housing

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics

SAMPLE DESIGN: Systematic sample of every 10th household with a random start, drawn by the Minnesota Population Center

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 268,248

Mode of data collection

Face-to-face [f2f]

Research instrument

Single record that includes housing and population questionnaires

Abstract

To ensure the centrality of refugees’ voices in discussions about their future, as well as to inform evidence-based inter-agency responses in support of host Governments, UNHCR is leading the regular implementation of intentions surveys with refugees from Ukraine, collecting primary data on their profiles, their current situation and intentions, and the factors influencing their decision-making.

The first, second and third regional intentions surveys were completed and the reports published in July 2022 (https://data.unhcr.org/en/documents/details/94176), September 2022 (https://data.unhcr.org/en/documents/details/95767) and February 2023 (https://data.unhcr.org/en/documents/details/99072). This data was collected during the fourth round, conducted between April and May 2023. The survey covered refugees hosted in countries in Europe.

A mixed methodological approach was used, combining two data collection modes. Around 3,850 refugee households were interviewed either through a phone-based survey, web-based survey or face-to-face interview. The data include a mix of Fresh refugee households (i.e. not included in previous rounds) and Panel households (i.e. those included in at least one of the previous rounds). All surveys used a harmonized questionnaire.

This data is an anonymous version of the original data collected and used for the primary analysis.

Geographic coverage

Europe

Analysis unit

Households

Universe

Refugees from Ukraine

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample includes households and individuals who completed this round as well as previous rounds (two and/or three) of the study (sample_type='Panel') and those who only participated in this round (sample_type = 'Fresh'). See more details in the report.

Mode of data collection

Other [oth]

Currently bibliographic databases have included a large number of Early Access (EA) articles. Taking 47 IEEE journals as examples, this study analyzed and compared the differences in publication stages of EA articles in three typical bibliographic databases, including Web of Science Core Collection, Scopus, and Engineering Village Compendex. Qualitative analysis of data sets that may appear in these three databases and their publication stage modes, and quantitative analysis on the number of records, proportion, and journal distributions of each data set and each publication stage mode were conducted. There were totally 7 sub-data sets and corresponding 26 publication stage modes, with 14 “undifferentiated publication stage modes” and 12 “differentiated publication stage modes”. Although the proportion of EA records from each “differentiated publication stage mode” was mostly below 1.0%, the absolute quantity of EA records with differences in the publication stage was noteworthy reaching 2516. Among the 47 journals, 23 journals have 7–8 publication stage modes, 1 journal having 18 modes, and 40 journals have one or more “differentiated publication stage modes”. Therefore, in IEEE journals, whether for the same EA article or the same journal, the difference in publication stage between these three databases was pervasive and complex.

RADARSAT-1 products The Standard beam mode operates with any one of seven beam positions, referred to as S1 to S7. The nominal incidence angle range covered by the full set of Standard beams is from 20 degrees (at the inner edge of S1) to 49 degrees (at the outer edge of S7). Each individual beam covers a minimum ground swath of 100 km within the total 500 km accessibility swath of the full set of Standard beams. The nominal spatial resolution in the range direction is 26 m for S1 at near range to 20 m for S7 at far range. The nominal azimuth resolution is the same, 27 m, for all beam positions. The Wide beam modes are similar to the Standard beams except that the swath width achieved by this beam is 150 km rather than 100 km. As a result, only three Wide beams, W1, W2 and W3 are necessary to provide coverage of almost all of the 500 km swath range. They provide comparable resolution to the standard beam mode, though the increased ground swath coverage is obtained at the expense of a slight reduction in overall image quality. In the Fine beam mode the nominal azimuth resolution is 8.4 m, with range resolution 9.1 m to 7.8 m from F1 to F5. Since the radar operates with a higher sampling rate in this mode than in any of the other beam mode, the ground swath coverage has to be reduced to approximately 50 km in order to keep the downlink signal within its allocated bandwidth. Originally, five Fine beam positions, F1 to F5, were available to cover the far range of the swath with an incidence angle range from 37 to 47 degrees. By modifying timing parameters, 10 new positions have been added with offset ground coverage. Each original Fine beam position can either be shifted closer to or further away from Nadir. In Extended High beam mode six positions, EH1 to EH6, are available for collection of data in the 49 to 60 degree incidence angle range. Since this beam mode operates outside the optimum scan angle range of the SAR antenna, some minor degradation of image quality can be expected when compared with the Standard beam mode. Swath widths are restricted to a nominal 80 km for the inner three positions, and 70 km for the outer three positions. In Extended Low beam mode one position, EL1, is provided for imaging in the incidence angle range 10 to 23 degrees with nominal ground swath coverage of 170 km. As with the Extended High beam mode, some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum elevation angle range. In ScanSAR mode, combinations of two, three or four single beams are used during data collection. Each beam is selected sequentially so that data is collected from a wider swath than possible with a single beam. The beam switching rates are chosen to ensure at least one 'look' at the Earth's surface for each beam within the along track illumination time or dwell time of the antenna beam. In practice, the radar beam switching is adjusted to provide two looks per beam. The beam multiplexing inherent in ScanSAR operation reduces the effective sampling rate within each of the component beams; hence the increased swath coverage is obtained at the expense of spatial resolution. The ScanSAR Narrow mode combines two beams (incidence angle range of 20 to 39 degrees) or three beams (incidence angle from 31 to 46 degrees) and provides coverage of a nominal 300 km ground swath, with spatial resolution of 50 m. The ScanSAR Wide mode combines four beams, provides coverage of either 500 km (with incidence angle range of 20 to 49 degrees) or 450 km (incidence angle range from 20 to 46 degrees) nominal ground swaths depending on the beam combination. Beam Mode Product Ground coverage (km2) Nominal resolution (m) Polarisation ScanSAR wide SCW, SCF, SCS 500 x 500 100 Single and dual ScanSAR narrow SCN, SCF, SCS 300 x 300 60 Single and dual Wide SGF, SGX, SLC, SSG, SPG 150 x 150 24 Single and dual Standard SGF, SGX, SLC, SSG, SPG 100 x 100 24 Single Extended low SGF, SGX, SLC, SSG, SPG 170 x 170 24 Single Extended high SGF, SGX, SLC, SSG, SPG 75 x 75 24 Single Fine SGF, SGX, SLC, SSG, SPG 50 x 50 8 Single RADARSAT-2 products The Standard Beam Mode allows imaging over a wide range of incidence angles with a set of image quality characteristics which provides a balance between fine resolution and wide coverage, and between spatial and radiometric resolutions. Standard Beam Mode operates with any one of eight beams, referred to as S1 to S8. The nominal incidence angle range covered by the full set of beams is 20 degrees (at the inner edge of S1) to 52 degrees (at the outer edge of S8). Each individual beam covers a nominal ground swath of 100 km within the total standard beam accessibility swath of more than 500 km. The Wide Swath Beam Mode allows imaging of wider swaths than Standard Beam Mode, but at the expense of slightly coarser spatial resolution. The three Wide Swath beams, W1, W2 and W3, provide coverage of swaths of approximately 170 km, 150 km and 130 km in widt...

Burst mode medium frequency, high amplitued calibrated electric field data from selected time periods

The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed. Interview Mode of collection: mixed mode Face-to-face interview: CAPI (Computer Assisted Personal Interview) Face-to-face interview: PAPI (Paper and Pencil Interview) Telephone interview: CATI (Computer Assisted Telephone Interview) Self-administered questionnaire: CAWI (Computer-Assisted Web Interview) Self-administered questionnaire: Paper In all countries, fieldwork was conducted on the basis of detailed and uniform instructions prepared by the WVS Scientific Committee and WVSA secretariat. The main data collection mode in 1981-2012 was face to face (interviewer-administered) interview with the printed questionnaire. Postal surveys (respondent-administered) have been used in Canada, New Zealanda, Japan, Australia. CAPI and online data collection modes have been introduced first in WVS-6 in 2012-2014. The main data collection mode in WVS 2017-2021 is face to face (interviewer-administered). Several countries employed mixed-mode approach to data collection: USA (CAWI; CATI); Australia and Japan (CAWI; postal survey); Hong Kong SAR (PAPI; CAWI); Malaysia (CAWI; PAPI). The WVS Master Questionnaire is always provided in English and each national survey team has to ensure that the questionnaire was translated into all the languages spoken by 15% or more of the population in the country. A central team monitors the translation process. The target population is defined as: individuals aged 18 (16/17 is acceptable in the countries with such voting age) or older (with no upper age limit), regardless of their nationality, citizenship or language, that have been residing in the [country] within private households for the past 6 months prior to the date of beginning of fieldwork (or in the date of the first visit to the household, in case of random-route selection). The sampling procedures differ from country to country; probability Sample: Multistage Sample Probability Sample, Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 1981-2020. In 1981-2012, the required sample size for each coutnry was N=1000 or above. In 2017-2021, the sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. As an exception, few surveys with smaller sample sizes have been accepted into the WVS 1981-2020 through the WVSA's history. Sample design and other relevant information about sampling are reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling is documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

Currently bibliographic databases have included a large number of Early Access (EA) articles. Taking 47 IEEE journals as examples, this study analyzed and compared the differences in publication stages of EA articles in three typical bibliographic databases, including Web of Science Core Collection, Scopus, and Engineering Village Compendex. Qualitative analysis of data sets that may appear in these three databases and their publication stage modes, and quantitative analysis on the number of records, proportion, and journal distributions of each data set and each publication stage mode were conducted. There were totally 7 sub-data sets and corresponding 26 publication stage modes, with 14 “undifferentiated publication stage modes” and 12 “differentiated publication stage modes”. Although the proportion of EA records from each “differentiated publication stage mode” was mostly below 1.0%, the absolute quantity of EA records with differences in the publication stage was noteworthy reaching 2516. Among the 47 journals, 23 journals have 7–8 publication stage modes, 1 journal having 18 modes, and 40 journals have one or more “differentiated publication stage modes”. Therefore, in IEEE journals, whether for the same EA article or the same journal, the difference in publication stage between these three databases was pervasive and complex.

Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). Effective data analysis is necessary to extract significant spatial and temporal information. In this work, information regarding the long term variation of seasonal field environment conditions is explored using Hilbert-Huang transform (HHT) based analysis on the wireless sensor network data collection. The data collection network, consisting of 36 wireless nodes, covers an area of 100 square kilometres in Yanqing, the northwest of Beijing CBD, in China and data collection involves environmental parameter observations taken over a period of three months in 2011. The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). Both spatial and temporal properties of data explored by HHT analysis are demonstrated. Our research shows potential for better understanding the spatial-temporal relationships among environmental parameters using WSN and HHT.

The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed. Interview Mode of collection: mixed mode Face-to-face interview: CAPI (Computer Assisted Personal Interview) Face-to-face interview: PAPI (Paper and Pencil Interview) Telephone interview: CATI (Computer Assisted Telephone Interview) Self-administered questionnaire: CAWI (Computer-Assisted Web Interview) Self-administered questionnaire: Paper Web-based Interview In all countries, fieldwork was conducted on the basis of detailed and uniform instructions prepared by the WVS Scientific Committee and WVSA secretariat. The main data collection mode in 1981-2012 was face to face (interviewer-administered) interview with the printed questionnaire. Postal surveys (respondent-administered) have been used in Canada, New Zealanda, Japan, Australia. CAPI and online data collection modes have been introduced first in WVS-6 in 2012-2014. The main data collection mode in WVS 2017-2022 is face to face (interviewer-administered) interview with a printed or electronic questionnaire (CAPI). Several countries employed mixed-mode approach to data collection: USA (CAWI; CATI); Australia and Japan (CAWI; postal survey); Hong Kong SAR (PAPI; CAWI); Malaysia (CAWI; PAPI). The WVS Master Questionnaire is always provided in English and each national survey team has to ensure that the questionnaire was translated into all the languages spoken by 15% or more of the population in the country. A central team monitors the translation process. The target population is defined as: individuals aged 18 (16/17 is acceptable in the countries with such voting age) or older (with no upper age limit), regardless of their nationality, citizenship or language, that have been residing in the [country] within private households for the past 6 months prior to the date of beginning of fieldwork (or in the date of the first visit to the household, in case of random-route selection). The sampling procedures differ from country to country; probability Sample: Multistage Sample Probability Sample, Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 1981-2022. In 1981-2012, the required sample size for each coutnry was N=1000 or above. In 2017-2022, the sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. As an exception, few surveys with smaller sample sizes have been accepted into the WVS 1981-2022 through the WVSA's history. Sample design and other relevant information about sampling are reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling is documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Ukraine were selected based on the following criterion:

(a) smallholder maize growers Grain corn
Region: Cherkassy & Kiev

(b) smallholder sunflower growers
Region: Vinnitsa, Kiev & Cherkassy

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab.

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group)

o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The metropolitan, urban and rural population and all .administrative regional units. as defined in Official Europe Union Statistics (NUTS 2) covered proportionately the respective population aged 18 and above. The country was divided into an appropriate number of areas, grouping NUTS regions at whatever level appropriately. The NUTS covered in Italy were the following; Basilicata, Calabria, Campania, Emilia, Friuli, Venezia, Giulia, Lazio, Liguria, Lombardia, Marche, Milano, Molise e Abbruzzi, Puglie, Sardegna, Sicilia, Toscana, Trentino, Umbria, Valle d.Aosta/Piemonte, Veneto.

The basic sample design was a multi-stage, random probability sample. 100 sampling points were drawn with probability proportional to population size, for a total coverage of the country. The sampling points were drawn after stratification by NUTS 2 region and by degree of urbanisation. They represented the whole territory of the country surveyed and are selected proportionally to the distribution of the population in terms of metropolitan, urban and rural areas. In each of the selected sampling points, one address was drawn at random. This starting address forms the first address of a cluster of a maximum of 20 addresses. The remainder of the cluster was selected as every Nth address by standard random route procedure from the initial address. In theory, there is no maximum number of addresses issued per country. Procedures for random household selection and random respondent selection are independent of the interviewer.s decision and controlled by the institute responsible. They should be as identical as possible from to country, full functional equivalence being a must.

At every address up to 4 recalls were made to attempt to achieve an interview with the selected respondent. There was only one interview per household. The final sample size is 1,002 completed interviews.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

The rapid and massive dissemination of mobile phones in the developing world is creating new opportunities for the discipline of survey research. The World Bank is interested in leveraging mobile phone technology as a means of direct communication with poor households in the developing world in order to gather rapid feedback on the impact of economic crises and other events on the economy of such households.

The World Bank commissioned Gallup to conduct the Listening to LAC (L2L) pilot program, a research project aimed at testing the feasibility of mobile phone technology as a way of data collection for conducting quick turnaround, self-administered, longitudinal surveys among households in Peru and Honduras.

The project used face-to-face interviews as its benchmark, and included Short Message Service (SMS), Interactive Voice Response (IVR) and Computer Assisted Telephone Interviews (CATI) as test methods of data collection.

The pilot was designed in a way that allowed testing the response rates and the quality of data, while also providing information on the cost of collecting data using mobile phones. Researchers also evaluated if providing incentives affected panel attrition rates. The Honduras design was a test-retest design, which is closely related to the difference-in-difference methodology of experimental evaluation.

The random stratified multistage sampling technique was used to select a nationally representative sample of 1,500 households. During the initial face-to-face interviews, researchers gathered information on the socio-economic characteristics of households and recruited participants for follow-up research. Questions wording was the same in all modes of data collection.

In Honduras, after the initial face-to-face interviews, respondents were exposed to the remaining three methodologies according to a randomized scheme (three rotations, one methodology per week). Panelists in Honduras were surveyed for four and a half months, starting in February 2012.

Geographic coverage

Includes the entire national territory, with the exception of neighborhoods where access of interviewers is extremely difficult, due to lack of transportation infrastructure or for situations that threaten the physical integrity of the interviewers and supervisors (i.e. extremely high crime rate, warfare, etc.)

Analysis unit

• Households

Universe

All the households that exist in the neighborhoods of Honduras, as reported by the 2001 Census. Institutions such as military, religious or educational living quarters are not included in the universe.

Kind of data

Sample survey data [ssd]

Sampling procedure

Honduras did not have an income oversample because the poverty rate is 60 percent, so oversampling 20 percent above the poverty rate would include a large portion of the middle class, which are not the most vulnerable in times of crisis.

The Honduras panel was built on a nationally representative sample of 1,500 households. The sample was drawn by means of a random, stratified, multistage design. The pilot used Gallup World Poll sampling frame.

Census-defined municipalities were classified into five strata according to population size: I. Municipalities with 500,000 to 999,000 inhabitants II. Municipalities with 100,000 to 499,000 inhabitants III. Municipalities with 50,000 to 99,000 inhabitants IV. Municipalities with 10,000 and 49,000 inhabitants V. Municipalities with less than 10,000 inhabitants

Interviews were then proportionally allocated to these five strata according to their share among the country's population.

• The first stage of the design consisted of a random selection of Primary Sampling Units (PSU's) within each of the five strata previously defined.

• In the second stage, in each PSU, one or more Secondary Sampling Units (SSU's) were then selected.

• Once SSU's were selected, interviewers were sent to the field to proceed with the third stage of the sample design, which consisted of selecting households using a systematic "random route" procedure. Interviewers started from the previously selected "random origin" and walked around the block in clockwise direction, selecting every third household on their right hand side. They were also trained to handle vacant, nonresponsive, non-cooperative households, as well as other failed attempts, in a systematic manner.

Mode of data collection

Other [oth]

Research instrument

The following survey instruments were used in the project:

1) Initial face-to-face questionnaire

In Peru, the starting point was the ENAHO (National Household Survey) questionnaire. Step-wise regressions were done to select the set of questions that best predicted consumption. For the purposes of robustness, the regressions were also done with questions that best predicted income, which yielded the same results. A similar procedure was done in Honduras, using the latest household survey deployed by the Honduran Statistics Institute, except that only best predictors of income were chosen, because Honduras did not have a recent consumption aggregate.

The survey gathered information on households' demographics, household infrastructure, employment, remittances, income, accidents, food security, self-perceptions on poverty, Internet access and cellphones use.

2) Monthly questionnaires (SMS, IVR, CATI)

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters. A maximum of 10 questions had to be chosen for the monthly questionnaire. In addition, two questions sought to ensure the validity of the responses by testing if the respondent was a member of the household. Most questions were time-variant and each questionnaire was repeated to observe if answers changed over time. All questions related to variables that strongly affect household welfare and are likely to change in times of crisis.

3) Final face-to-face questionnaire

Gallup conducted face-to-face closing surveys among 700 panelists. The researchers asked about issues the respondets had with mobile phones and coverage during the test. Panelists were also asked what would motivate them to keep on participating in a project like this in the future.

The questionnaires were worded exactly the same way, regardless of the mode, which meant short questions, since SMS is limited to 160 characters, unlike IVR and CATI.

Response rate

In Honduras, 41% of recruited households failed to answer the first round of follow-up surveys. The attrition rate from the initial face-to-face interview to the end of panel study was 50%.

As part of the survey administration process Gallup implemented a number of mechanisms to maximize the response rate and panelist retention. The following strategies were applied to respondents who did not replay first time:

• The surveys were left open for responses for up to 2 weeks after the original transmission of the survey (from original call in the case of IVR and CATI).
• First reminder was sent within 72 hours of first attempt (SMS and IVR).
• Second reminder was sent within 144 hours of first attempt (SMS and IVR).
• Call backs were made within 72 and 144 hours of first attempt (CATI); or
• Up to 2 call backs were made per appointment with respondent (CATI).

Also, in order to minimize non-response, three types of incentives were given. First, households that did not own a mobile phone were provided one for free. Approximately 127 phones were donated in Honduras. Second, all communications between the interviewers and the households were free to the respondents. Finally, households were randomly assigned to one of three incentive levels: one-third of households received US$1 in free airtime for each questionnaire they answered, one-third received US$5 in free airtime, and one-third received no financial incentive (the control group).

Abstract

Since its inception in the mid-1990s, the Multiple Indicator Cluster Surveys programme, known as MICS, has become the largest source of statistically sound and internationally comparable data on children and women worldwide. In countries as diverse as Bangladesh, Thailand, Fiji, Qatar, Cote d’Ivoire, Turkmenistan and Argentina, trained fieldwork teams conduct face-to-face interviews with household members on a variety of topics – focusing mainly on those issues that directly affect the lives of children and women. MICS is an integral part of plans and policies of many governments around the world, and a major data source for more than 30 Sustainable Development Goals (SDGs) indicators. The MICS programme continues to evolve with new methodologies and initiatives, including MICS Plus, MICS Link, MICS GIS and the MICS Tabulator.

Geographic coverage

Kosovo under UNSC res. 1244 (Roma, Ashkali, and Egyptian Communities) The majority of MICS surveys are designed to be representative at the national level. Sample sizes are sufficient to generate robust data at the regional or provincial levels, and for urban and rural areas. Subnational surveys, covering specific population groups (such as Palestinians in Lebanon) or specific geographical areas (such as selected regions of East in Afghanistan) within countries are also conducted.

Analysis unit

Household, Individual

Sampling procedure

Sample sizes vary greatly from one survey to the other, currently averaging around 12,000 households (for national surveys).

The sample for the Multiple Indicator Cluster Survey (MICS) was designed to provide estimates on a large number of indicators on the situation of children and women at the national level, for areas of residence, and for geographical locations, such as regions, governorates, or districts. A multi-stage, stratified cluster sampling approach was typickly used for the selection of the survey sample. MICS6 surveys are not self-weighting. For reporting national level results, sample weights were used. A more detailed description of the sample design can be found in Appendix A of Final Report.

Mode of data collection

Face-to-face [f2f]

Research instrument

MICS questionnaires were designed by implementing agencies, typically the National Statistical Offices. In each country, MICS questionnaires were based on an assessment of the country’s data needs. The starting point were the standard MICS questionnaires designed by UNICEF’s Global MICS Team, in close coordination with experts, development partners and other international survey programmes. Countries chose from the MICS modules in the standard MICS questionnaires. UNICEF’s MICS experts supported implementing agencies to customize the questionnaires, as required, to the national setting. All survey activities, from sample and survey design, to fieldwork and report writing are carried out by the implementing agencies – with continuous technical support from UNICEF.

The sixth round of MICS included six model questionnaires: • Household Questionnaire • Water Quality Testing Questionnaire • The Questionnaire for Individual Women • The Questionnaire for Individual Men • The Questionnaire for Children Age 5-17 and • The Questionnaire for Children Under Five

The flexible, modular nature of MICS questionnaires makes it easy to remove modules which may not be relevant, and modules for which there is already good quality data from other sources.

Refer to tools page on mics.unicef.org for more detailed information on the flow of questionnaires and contents of the modules.